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85 行
2.9 KiB
85 行
2.9 KiB
import argparse
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import numpy as np
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from mlagents_envs.environment import UnityEnvironment
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from mlagents_envs.side_channel.engine_configuration_channel import (
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EngineConfigurationChannel,
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)
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def main(env_name):
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"""
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Run the low-level API test using the specified environment
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:param env_name: Name of the Unity environment binary to launch
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"""
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engine_configuration_channel = EngineConfigurationChannel()
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env = UnityEnvironment(
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file_name=env_name,
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side_channels=[engine_configuration_channel],
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no_graphics=True,
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args=["-logFile", "-"],
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)
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try:
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# Reset the environment
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env.reset()
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# Set the default brain to work with
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group_name = env.get_agent_groups()[0]
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group_spec = env.get_agent_group_spec(group_name)
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# Set the time scale of the engine
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engine_configuration_channel.set_configuration_parameters(time_scale=3.0)
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# Get the state of the agents
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step_result = env.get_step_result(group_name)
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# Examine the number of observations per Agent
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print("Number of observations : ", len(group_spec.observation_shapes))
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# Is there a visual observation ?
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vis_obs = any(len(shape) == 3 for shape in group_spec.observation_shapes)
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print("Is there a visual observation ?", vis_obs)
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# Examine the state space for the first observation for the first agent
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print("First Agent observation looks like: \n{}".format(step_result.obs[0][0]))
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for _episode in range(10):
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env.reset()
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step_result = env.get_step_result(group_name)
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done = False
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episode_rewards = 0
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while not done:
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if group_spec.is_action_continuous():
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action = np.random.randn(
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step_result.n_agents(), group_spec.action_size
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)
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elif group_spec.is_action_discrete():
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branch_size = group_spec.discrete_action_branches
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action = np.column_stack(
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[
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np.random.randint(
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0, branch_size[i], size=(step_result.n_agents())
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)
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for i in range(len(branch_size))
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]
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)
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else:
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# Should never happen
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action = None
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env.set_actions(group_name, action)
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env.step()
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step_result = env.get_step_result(group_name)
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episode_rewards += step_result.reward[0]
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done = step_result.done[0]
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print("Total reward this episode: {}".format(episode_rewards))
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finally:
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env.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--env", default="Project/testPlayer")
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args = parser.parse_args()
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main(args.env)
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